Computational Vision Coursera Quiz Answers 2022 | All Weeks Assessment Answers [💯Correct Answer]

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Here, you will find Computational Vision Exam Answers in Bold Color which are given below.

These answers are updated recently and are 100% correct✅ answers of all week, assessment, and final exam answers of Computational Vision from Coursera Free Certification Course.

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About Computational Vision Course

In this course, we will expand on vision as a cognitive problem space and explore models that address various vision tasks. We will then explore how the boundaries of these problems lead to a more complex analysis of the mind and the brain and how these explorations lead to more complex computational models of understanding.

WHAT YOU WILL LEARN

• Apply various models of human and machine vision and discuss their limitations.
• Demonstrate the geon model of object recognition and its limitations.
• Argue the benefits and drawbacks of the symbolist and visualise perspectives of mental imagery.
• Recognize the single-layer and multi-layer perceptron neural network models of artificial intelligence.

Course Apply Link – Computational Vision

Computational Vision Quiz Answers

Week 1 Quiz Answers

Quiz 1: Vision Overview

Q1. The human eye, to a first approximation, works like which of the following?

• A​ notebook
• A​ pinhole camera
• A​ recording device
• A​ sketch Artist

Q2. The “piano-in-the-mirror” example illustrates which of the following ideas?

• It is impossible in principle to recover a unique three-dimensional structure from a two-dimensional projection.
• The eye’s retina is unreliable in its operation.
• Mirrors pose a particular challenge to the human vision system.
• Optical illusions reveal the strengths of the human vision system.

Q3. Which of these does not represent a potential complicating factor for our “pixel-array” portrait of the retina?

• There are many wavelengths that the eye is not responsive to.
• Color vision can provide useful information for interpreting an image.
• The fovea has higher resolution than the periphery of the retina, so our “evenly distributed array” portrait is inaccurate.
• Binocular vision can provide useful information for interpreting an image.

Q4. As a very first step in treating vision as a computational problem, we can think of a retinal image as:

• A photograph.
• A small copy of the object being attended to.
• An array of pixels, where each pixel denotes a light intensity value.
• A line sketch of the object being attend to.

Q5. Despite the simplicity of our first model of vision – interpreting black and white photos – it is not entirely unfair because:

• Binocular vision is generally of little use.
• It is, after all, a task that we as human beings are capable of.
• Most scenes in real life do not involve information such as motion.
• Many animals have limited color vision.

Q6. Optical illusions are useful tools for studying the computational view of vision because:

• They are entertaining illustrations of how odd the visual world is.
• They highlight “gaps” in our vision algorithms – situations where the algorithms give the wrong answers.
• They show that we are not as good at “seeing” as we think.
• They show that our color vision is faulty.

Week 2

Quiz 1: Edges

Q1. When considering an image as a 2D array of pixels, what denotes an edge?

• A line of pixels with similar intensity.
• A high average intensity level among surrounding pixels.
• A very high intensity pixel.
• A line of pixels with adjacent pixels with very different intensity levels (high and low values).

Q2. If our convolution function is centered on a low-intensity (dark) pixel along a dark-to-light transition, what kind of value will the function output?

• A positive number.
• A negative number.
• We don’t have enough information to determine the answer.
• 0​

Q3. Which statement best represents the relationship between human vision and the convolution function approach to edge detection?

• Photoreceptors are sensitive to transitions between high and low intensity of light, like a convolution function, rather than just intensity, like a pixel.
• The retinal ganglion cells perform a similar function to the convolution function, looking for differences in signal from an area of photoreceptor cells.
• Retinal ganglion cells do not use a convolution approach, instead, they take an average of inputs from local photoreceptors then poll nearby ganglion cells to look for differences.
• The convolution approach is not a good representation of human vision.

Quiz 2: Geons

Q1. Suppose, as a rough estimate, we say that there are 20 distinct geons used for object recognition; and each geon can come in 5 classifiable qualitative sizes (tiny, small, moderate, large, huge); and a pair of geons can be placed in 10 distinct qualitative relations (geon A on top of geon B; geon A to upper left of geon B; geon A to the left of geon B; and so forth).

How many distinct two-geon objects do we have in the space described above?

Enter answer here

Q2. Now, suppose we add a third geon, geon C. Again, each geon comes in 20 varieties and 5 sizes. We’ll start by creating a two-geon pair of A and B just like in Question 1 above; then, we decide which of A or B the third geon (C) will be adjacent to, and then we place geon C beside either A or B in one of the 10 allowed relations. How many distinct three-geon objects do we have in this space?

Enter answer here

Q3. Are these numbers (as an estimate of the “dictionary size” of potential two-geon and three-geon objects) significantly bigger, significantly smaller, or comparable to the actual size of our object vocabulary in English as discussed in lecture?

• much ​bigger
• much s​maller
• about the s​ame

Week 3

Quiz 1: Mental Imagery

Q1. Explain the significance of the following experiment (described in lecture and/or the reading from Finke) to the debates surrounding mental imagery. Does the experiment better support the “symbolist” camp or “visualist / pictoralist” camp as discussed in lecture?

Shepard-Metzler “mental rotation” experiment.

• S​ymbolist
• V​isualist

Q2. Ambiguous figure (duck/rabbit) mental imagery experiment

• S​ymbolist
• V​isualist

Q3. Finke experiment (mental imagery and resolution of grids of lines)

• S​ymbolist
• V​isualist

Q4. Mental imagery and the McCollough effect

• S​ymbolist
• V​isualist

Week 4

Quiz 1: Convolution Problem

Q1. Consider the following matrix representation of a 4 pixel by 4 pixel black and white image, which we will call A:

1	1	0	1
0	1	0	1
1	1	1	0
1	1	0	0

And the edge detection matrix B:

-1	-1	-1
-1	8	-1
-1	-1	-1

If we convolve matrix A and Matrix B, what are the values in the resulting matrix?

For this answer, write your answer in the form:

A, B, C, D

Where each letter is replaced with the numeric value that would be found in this matrix representation:

A	B
C	D

Conclusion

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